Fake Image Identification

Venkat Java Projects
Mobile:+91 9966499110
Visit:www.venkatjavaprojects.com Email:venkatjavaprojects@gmail.com
Fake Image Identification
Now-a-days biometric systems are useful in recognizing person’s identity but
criminals change their appearance in behaviour and psychological to deceive
recognition system. To overcome from this problem we are using new technique
called Deep Texture Features extraction from images and then building train
machine learning model using CNN (Convolution Neural Networks) algorithm.
This technique refer as LBPNet or NLBPNet as this technique heavily dependent
on features extraction using LBP (Local Binary Pattern) algorithm.
In this project we are designing LBP Based machine learning Convolution Neural
Network called LBPNET to detect fake face images. Here first we will extract
LBP from images and then train LBP descriptorimages with Convolution Neural
Network to generate training model. Whenever we upload new test image then
that test image will be applied on training model to detect whether test image
contains fake image or non-fake image. Below we can see some details on LBP.
Local binary patterns (LBP) is a type of visual descriptor used for classification
in computervision and is a simple yet very efficient texture operatorwhich labels
the pixels of an image by thresholding the neighborhood of each pixel and
considers the result as a binary number. Due to its discriminative power and
computational simplicity, LBP texture operator has become a popular approach
in various applications. It can be seen as a unifying approachto the traditionally
divergent statistical and structural models of texture analysis. Perhaps the most
important propertyofthe LBP operatorin real-world applications is its robustness
to monotonic gray-scale changes caused, forexample, by illumination variations.
Another important property is its computational simplicity, which makes it
possible to analyze images in challenging real-time settings.
The LBP feature vector, in its simplest form, is created in the following manner:
Divide the examined window into cells (e.g. 16x16 pixels for each cell).
For each pixel in a cell, compare the pixel to each of its 8 neighbors (on its left-
top, left-middle, left-bottom, right-top, etc.). Follow the pixels along a circle, i.e.
clockwise or counter-clockwise.
Where the center pixel's value is greater than the neighbor's value, write "0".
Otherwise, write "1". This gives an 8-digit binary number (which is usually
converted to decimal for convenience).

Mobile:+91 9966499110
Compute the histogram, over the cell, of the frequency of each "number"
occurring (i.e., each combination of which pixels are smaller and which are
greater than the center). This histogram can be seen as a 256-dimensional feature
vector.
Optionally normalize the histogram.
Concatenate (normalized) histograms of all cells. This gives a feature vector for
the entire window.
The feature vector can now be processed using the Support vector machine,
extreme learning machines, orsome other machine learning algorithm to classify
images. Such classifiers can be used for face recognition or texture analysis.
A useful extension to the original operator is the so-called uniform pattern,[8]
which can be used to reduce the length of the feature vector and implement a
simple rotation invariant descriptor. This idea is motivated by the fact that some
binary patterns occur more commonly in texture images than others. A local
binary pattern is called uniform if the binary pattern contains at most two 0-1 or
1-0 transitions. For example, 00010000 (2 transitions) is a uniform pattern, but
01010100 (6 transitions) is not. In the computation of the LBP histogram, the
histogram has a separate bin for every uniform pattern, and all non-uniform
patterns are assigned to a single bin. Using uniform patterns, the length of the
feature vector for a single cell reduces from 256 to 59. The 58 uniform binary
patterns correspond to the integers 0, 1, 2, 3, 4, 6, 7, 8, 12, 14, 15, 16, 24, 28, 30,
31, 32, 48, 56, 60, 62, 63, 64, 96, 112, 120, 124, 126, 127, 128, 129, 131, 135,
143, 159, 191, 192, 193, 195, 199, 207, 223, 224, 225, 227, 231, 239, 240, 241,
243, 247, 248, 249, 251, 252, 253, 254 and 255.
CNN working procedure
To demonstrate how to build a convolutional neural network based image
classifier, we shall build a 6 layer neural network that will identify and separate
one image from other. This network that we shall build is a very small network
that we can run on a CPU as well. Traditional neural networks that are very good
at doing image classification have many more parameters and take a lot of time
if trained on normal CPU. However, our objective is to show how to build a real-
world convolutional neural network using TENSORFLOW.
Neural Networks are essentially mathematical models to solve an optimization
problem. They are made of neurons, the basic computation unit of neural
networks. A neuron takes an input (say x), do some computation on it (say:
multiply it with a variable w and adds another variable b)to produceavalue (say;
z= wx + b). This value is passed to anon-linear function called activation function

Mobile:+91 9966499110
(f) to produce the final output (activation) of a neuron. There are many kinds of
activation functions. One of the popular activation function is Sigmoid. The
neuron which uses sigmoid function as an activation function will be called
sigmoid neuron. Depending on the activation functions, neurons are named and
there are many kinds of them like RELU, TanH.
If you stack neurons in a single line, it’s called a layer; which is the next building
block of neural networks. See below image with layers
To predict image class multiple layers operate on each other to get best match
layer and this process continues till no more improvement left.
Dataset Details:
In this paper author has used NUAA Photograph Imposter (fake) Database with
images obtained from real and fake faces. We also used images and convert that
image into LBP format. Below are some images from LBP folder

Mobile:+91 9966499110
All this fake and real images you can see inside ‘LBP/train’ folder.
This project consists of following modules:
1) Generate NLBPNet Train & Test Model: in this module we will read all
LBP images from LBP folder and then train CNN model with all those
images.
2) Upload Test Image: In this module we will upload test image from
‘testimages’ folder. Application will read this image and then extract Deep
Textures Features from this image using LBP algorithm.
3) Classify Picture In Image: This module apply test image on CNN train
model to predict whether test image contains spoof or non-spoof face.
Screen shots
To run this project double click on ‘run.bat’ file to get below screen

Mobile:+91 9966499110
In abovescreen click on ‘Generate Image Train & TestModel’ buttonto generate
CNN model using LBP images contains inside LBP folder.

Mobile:+91 9966499110
In above screen we can see CNN LBPNET model generated. Now click on
‘Upload Test Image’ button to upload test image
In above screen we can see two faces are there from same person but in different
appearances. For simplicity I gave image name as fake and real to test whether
application can detect it or not. In above screen I am uploading fake image and
then click on ‘Classify Picture In Image’ button to get below result

Mobile:+91 9966499110
In above screen we can see all real face will have normal light and in fake faces
peoples will try some editing to avoid detection but this application will detect
whether face is real or fake
In above screen I am uploading 1.jpg and after upload click on openbutton to get
below screen

Mobile:+91 9966499110
And now click on ‘classify Picture in Image’ to get below details
In abovescreen we are getting result as image contains Fake face. Similarly u can
try other images also. If u want to try new images then u need to send those new
images to us so we will make CNN model to familiar with new images so it can
detect those images also.

Fake Image Identification

Recommended

Recommended

More Related Content

What's hot

What's hot (20)

Similar to Fake Image Identification

Similar to Fake Image Identification (20)

More from Venkat Projects

More from Venkat Projects (20)

Recently uploaded

Recently uploaded (20)

Fake Image Identification